Crop identification by massive processing of multiannual satellite imagery for EU common agriculture policy subsidy control. Issue 1 (1st January 2021)

• Record Type: Journal Article
• Authors: Lozano-Tello, Adolfo; Fernández-Sellers, Marcos; Quirós, Elia; Fragoso-Campón, Laura; García-Martín, Abelardo; Gutiérrez Gallego, José Antonio; Mateos, Carmen; Trenado, Rubén; Muñoz, Pedro
• Is Part Of: European journal of remote sensing. Volume 54:Issue 1(2021)
• Page Start: 1
• Page End: 12
• Publication Date: 2021-01-01
• Subjects: Crop identification -- neural networks -- multitemporal analysis -- pixel-based identification -- remote sensing -- eu Common Agricultural Policy subsidy control; Remote sensing -- Periodicals; Remote sensing; Electronic journals; Periodicals; 621.3678
• DOI: 10.1080/22797254.2020.1858723
• Languages: English
• ISSNs: 2279-7254

Abstract

ABSTRACT: The early and automatic identification of crops declared by farmers is essential for streamlining European Union Common Agricultural Policy (CAP) payment processes. Currently, field inspections are partial, expensive and entail a considerable delay in the process. Chronological satellite images of cultivated plots can be used so that neural networks can form the model of the declared crop. Once the patterns of a crop are obtained, the correspondence of the declaration with the model of the neural network can be systematically predicted, and can be used for monitoring the CAP. In this article, we propose a learning model with neural networks, using as examples of training the pixels of the cultivated plots from the satellite images over a period of time. We also propose using several years in the training model to generalise the patterns without linking them to the climatic characteristics of a specific year. The article also describes the use of the model in learning the multi-year pattern of tobacco cultivation with very good results.